Agentic AI Architect

Title:

Agentic AI Architect

Location:

Dallas or Houston, TX (hybrid: 2 days onsite / 3 days remote)
Duration:
Direct Hire
Compensation: $190K - $220K per year
Work Requirements: US Citizen, GC Holders or Authorized to Work in the U.S.

• Define and evolve the enterprise AI platform architecture, breaking down business and functional requirements into scalable, secure, and maintainable technical designs.
• Establish the long-term architecture runway for AI products, owning POCs, design spikes, and forward-looking evaluations of emerging technologies— including agentic architectures, orchestration frameworks, and tool-use patterns.
• Apply expertise in distributed systems and API design to ensure the platform is robust, observable, and simple to extend.
• Ensure best practices in secure coding, performance optimization, resilience, and lifecycle maintainability.

• Architect and implement enterprise-grade RAG (Retrieval Augmented Generation) pipelines, including vector search, embeddings, knowledge stores, and LLM orchestration.
• Design and operationalize agentic workflows, including multi-agent collaboration, tool-calling agents, planner/executor patterns, and automated reasoning loops.
• Build and integrate production-grade AI agents capable of interacting with external tools, APIs, enterprise systems, and knowledge sources.
• Utilize the Model Context Protocol (MCP) to expose internal tools, datasets, and enterprise APIs to LLMs in a secure, governed manner.
• Integrate and operationalize multiple LLM providers (Azure OpenAI, OpenAI, Anthropic, open-source models) into production systems.
• Leverage Azure AI services— Semantic Kernel, Kernel Memory, AI Foundry, Cognitive Search—to enable intelligent, context-aware platform capabilities.
• Design, deploy, and optimize cloud-native AI applications in Azure with emphasis on cost efficiency, observability, resilience, and security.

• Partners with enterprise architects, product managers, developers, and data engineers in an Agile/Scrum environment to ensure architecture aligns with product strategy.
• Provide hands-on mentorship on AI development patterns—including agent-building, RAG troubleshooting, prompt design, vector search patterns, and orchestration frameworks.
• Conduct architecture reviews, code reviews, and design sessions to drive engineering quality and consistency.
• Translate business needs into scalable platform capabilities that support long-term product and enterprise roadmaps.

• Stay current on rapidly evolving AI trends: multi-agent systems, model orchestration, tool-use protocols, evaluation frameworks, prompt engineering, latency optimization, and AI safety approaches.
• Recommend improvements to development, testing, deployment, and estimation processes to increase delivery velocity while maintaining quality and compliance.
• Establish platform-level governance patterns including architectural guardrails, reusable modules, MCP tool adapters, and standardized agent templates.
• Champion experimentation, rapid…